Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke

Swift diagnosis and treatment play a decisive role in the clinical outcome of patients with acute ischemic stroke (AIS), and computer-aided diagnosis (CAD) systems can accelerate the underlying diagnostic processes. Here, we developed an artificial neural network (ANN) which allows automated detection of abnormal vessel findings without any a-priori restrictions and in <2 minutes. Pseudo-prospective external validation was performed in consecutive patients with suspected AIS from 4 different hospitals during a 6-month timeframe and demonstrated high sensitivity (≥87%) and negative predictive value (≥93%). Benchmarking against two CE- and FDA-approved software solutions showed significantly higher performance for our ANN with improvements of 25–45% for sensitivity and 4–11% for NPV (p ≤ 0.003 each). We provide an imaging platform (https://stroke.neuroAI-HD.org) for online processing of medical imaging data with the developed ANN, including provisions for data crowdsourcing, which will allow continuous refinements and serve as a blueprint to build robust and generalizable AI algorithms.


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data provided by other academic researchers on their behalf (for research purposes only), following completion of a Material Transfer Agreement. Proposals and requests for data access should be directed to the corresponding author via email. A user-friendly end-to-end workflow of the developed ANN is publicly available (for research purposes only) through https://stroke.neuroAI-HD.org.
Data Collected and available in Data Supplement - Table 2. The research findings apply to both male and female sex, as both populations are included in the training and testing cohorts. The distribution of male:female is reported in Supplementary  Table 2 and shows non-significant differences across all datasets.
Demographic data on the patient population is listed in the Data Supplement - Table 2.
The Heidelberg cohort included 800 consecutive patients with AIS and confirmed vessel occlusion on CT-angiography who subsequently underwent EVT between 03/2010 and 02/2020, as well as 379 consecutive patients with a suspected diagnosis of stroke but no vessel occlusion (control group) who underwent CT-angiography between 10/2019 and 02/2020. Pseudoprospective external testing of the ANN was performed onto two different datasets, and namely (i) the FAST cohort, with 358 consecutive patients who underwent CT-angiography between 01/2022 and 06/2022 for suspected AIS at three primary/ secondary care hospitals of the regional stroke consortium Rhine-Neckar with acute teleneurology/teleradiology coverage through the Heidelberg University Hospital, and the UKB cohort, with 323 patients who underwent CT-angiography between 09/2020 and 04/2021 for suspected AIS at the Department of Neuroradiology of the Bonn University Hospital.
The study was approved by the ethics committee of the University of Heidelberg The internal sample size was determined by the availability of data in our records (all data available was included). The external data samples were collected by setting a fixed time period of six months and collecting all available data within said timeframe.
See Figure 1 in the Main manuscript body for a list of the patient exclusions from the data samples. Patients were excluded mainly based insufficient on data quality or data corruption.
After testing our tool on the internal sample, we used two external data cohorts to reproduce our findings. The ANN showed consistent results also in the external testing samples.
Not relevant to this study, no randomization was performed. The study is retrospective in nature and the same treatment was applied to the entire sample in all cases.
Not relevant to this study, no randomization was performed. The study is retrospective in nature and the same treatment was applied to the entire sample in all cases.